Instacart Market Basket Analysis
2019-03-07
- 1 Introduction to the dataset
- 2 Market Basket analysis
- 2.1 Regroup items per basket
- 2.2 View the distribution of orders / transactions (hours and week days)
- 2.3 plot the number of prior , train, and test orders
- 2.4 plot the number of items per order
- 2.5 Top costomers for Top products
- 2.6 Top reordered items
- 2.7 Which most items added the first to the card
- 2.8 reordering rate versus added rank to the cart
- 2.9 Which most pourcentage of items added the first to the card
- 2.10 Top90 sellers of Banana and Strawberries
- 2.11 Explore Days interval of reordering items
- 2.12 Explore number of orders and reordering items
- 2.13 Visualize Departments and Aisles
- 2.14 Look for proportion of costumers that reorders the same products
- 2.15 the basket for the most fidel user_id
1 Introduction to the dataset
The dataset for this competition is a relational set of files describing customers’ orders over time. The goal of the competition is to predict which products will be in a user’s next order. The dataset is anonymized and contains a sample of over 3 million grocery orders from more than 200,000 Instacart users. For each user, we provide between 4 and 100 of their orders, with the sequence of products purchased in each order. We also provide the week and hour of day the order was placed, and a relative measure of time between orders. For more information, see the blog post accompanying its public release.
1.1 Load Packages
1.2 Function for multiple plot
# Define multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols: Number of columns in layout
# - layout: A matrix specifying the layout. If present, 'cols' is ignored.
#
# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
# then plot 1 will go in the upper left, 2 will go in the upper right, and
# 3 will go all the way across the bottom.
#
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}1.3 Load files
aisles <- data.table::fread("aisles.csv")
departments <- data.table::fread("departments.csv")
order_prior <- data.table::fread("order_products__prior.csv")
order_train <- data.table::fread("order_products__train.csv")
orders <- data.table::fread("orders.csv")
products <- data.table::fread("products.csv")
sample_sub <- data.table::fread("sample_submission.csv")1.4 Files glimpse
The list of aisles 
The list of departments 
Example of sample submission 
The list of orders: order_dow’ is the day of week. user 1 has 11 orders, 1 of which is in the train set, and 10 of which are prior orders. 
The list of Products 
order_Prior: Contains previous order contents for all customers ‘reordered’ indicates that the customer has a previous order that contains the product 
Order_Train: in each order, products were added de the card by priority. some products were reordered
Orders Train
1.5 Reformating Datasets
aisles <- aisles %>%
mutate(aisle = as.factor(aisle))
departments <- departments %>%
mutate(department = as.factor(department))
order_prior <- order_prior %>%
mutate(reordered = as.logical(reordered)) #%>%
#mutate(product_id = as.factor(product_id))
order_train <- order_train %>%
mutate(reordered = as.logical(reordered))
orders <- orders %>%
mutate(eval_set = as.factor(eval_set)) %>%
mutate(w_day = wday(order_dow , label = TRUE)) %>% # +1: weekdays have the interval [0:6] an dnot [1:7]
mutate(user_id = as.factor(user_id))
products <- products %>%
mutate(product_name = as.factor(product_name))2 Market Basket analysis
In this first section, we would try explore the of details of the orders, the content of baskets, the best sold items.
2.1 Regroup items per basket
In each orders, costumers bougth multiple items forming a basket. We will regroup the orders per basket.
We need to join orders by order_id and then by products_id. This code take a while.
# transactions <- orders %>%
# left_join(order_prior, by = "order_id") %>%
# left_join(products, by = "product_id")
#
# baskets <- transactions %>%
# plyr::ddply(c("order_id", "user_id"),
# function(df1) paste(df1$product_name,
# collapse = ","))
#
# colnames(baskets) <- c("Order_id","user_id","Baskets")
# baskets <- readRDS("baskets.RDS")
# tibble::glimpse(baskets)
# saveRDS(object = baskets, file = "baskets.RDS")2.2 View the distribution of orders / transactions (hours and week days)
## time of ordering
p1 <- orders %>%
ggplot(aes(x = order_hour_of_day)) +
geom_histogram( stat="count", color= "blue") # ,bins = 24
## days of ordering
p2 <- orders %>%
ggplot(aes(x = w_day)) +
geom_histogram( stat= "count", color = "green")
## interval of days before Reordering
p3 <- orders %>%
ggplot(aes(x = days_since_prior_order)) +
geom_histogram(bins = 30, color = "yellow")
# plot P1, P2, P3, p4 in the same plot
layout <- matrix(c(1,2,3,3), 2, 2, byrow = TRUE)
multiplot(p1, p2, p3, layout=layout)
we find: The main orders were done during the working hours of the day (8:17h). We assume that NA corresponds to saturday. A clear effect of the orders number is shown durinr the weekend. We observe a interval of reordering of 30 days. During this cycle we observe a picks at the dat 7, 15 and 30. We can observe more ordering during weekends during a month. But we can aloso observe diffrence of the number of orderinf between weekends.
2.3 plot the number of prior , train, and test orders
## count the number of prior orders
p3 <- orders %>%
filter(eval_set == 'prior') %>%
ggplot(aes(order_number)) +
geom_histogram(stat = "count", color = "red")
p2 <- orders %>%
filter(eval_set == 'train') %>%
ggplot(aes(order_number)) +
geom_histogram(stat = "count", color = "green")
p1 <- orders %>%
filter(eval_set == 'test') %>%
ggplot(aes(order_number)) +
geom_histogram(stat = "count", color = "blue")
# plot P1, P2, P3 in the same plot
layout <- matrix(c(1,2,3,3), 2, 2, byrow = TRUE)
multiplot(p1, p2, p3, layout=layout)
We have more prior (200000) orders, than traiun (15000) than test (7500). We observe a pick at 100 orders number for test and train samples.
2.4 plot the number of items per order
prior <- order_prior %>%
group_by(order_id) %>%
dplyr::summarise(n_orders = n()) %>%
ggplot(aes(x= n_orders)) +
geom_histogram(bins = 50, color = "yellow")+
xlim(0,50) +
labs(title = "Prior orders") +
xlab("number of items per order") +
ylab("n° orders")
train <- order_train %>%
group_by(order_id) %>%
dplyr::summarise(n_orders = n()) %>%
ggplot(aes(x= n_orders)) +
geom_histogram(bins = 50, color = "orange") +
xlim(0,50) +
labs(title = "Train orders") +
xlab("number of items per order") +
ylab("n° orders")
# plot P1, P2, P3 in the same plot
layout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
multiplot(prior, train, layout=layout)
We find: The most frequent nbr of order is about 5-6 items for the Prior and Train datasets. Ordering 25 items seems to be an exception.
2.5 Top costomers for Top products
# top costumers that purchased the largest number of items
#the dataset is limited to 100 items maximum per costumer
top_costumers_items <- orders %>%
group_by(user_id) %>%
dplyr::summarise(n_orders = length(order_id)) %>%
filter(n_orders < 100) %>%
top_n(50, wt = n_orders) %>%
#arrange(freq) %>%
ggplot(aes(x = reorder(user_id, - n_orders), y = n_orders)) +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_point() +
labs(title = "Top costumers which buy more items") +
xlab("user_id") +
ylab("nbr items")
top_costumers_visits <- orders %>%
#filter(user_id == 123) %>%
group_by(user_id) %>%
dplyr::summarise(n_visits = last(order_number)) %>%
filter(n_visits < 100) %>%
top_n(20, n_visits) %>%
ggplot(aes(x = user_id, y = n_visits)) +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_col() +
labs(title = "Number of visits per costumers") +
xlab("User_id") +
ylab("Order Number")
top20_item_prior <- order_prior %>%
group_by(product_id) %>%
dplyr::summarise(n = n()) %>%
top_n(20, wt = n) %>%
left_join(products, by = 'product_id') %>%
ggplot(aes(x = reorder(product_name, - n) , y = n )) +
#coord_flip() +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_col( color = "green") +
labs(title = "Top 20 products for Prior orders") +
xlab("product_id") +
ylab("n° orders")
top20_item_train <- order_train %>%
group_by(product_id) %>%
dplyr::summarise(n = n()) %>%
top_n(20, wt = n) %>%
left_join(products, by = 'product_id') %>%
ggplot(aes(x = reorder(product_name, - n) , y = n )) +
#coord_flip() +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_col( color = "red") +
labs(title = "Top 20 products for train orders") +
xlab("product_id") +
ylab("nbr orders")
# plot P1, P2, P3, p4 in the same plot
layout <- matrix(c(1,2,3,4), 2, 2, byrow = TRUE)
multiplot(top_costumers_visits, top_costumers_visits, top20_item_prior,top20_item_train, layout=layout)
2.6 Top reordered items
reordered_train <- order_train %>%
filter(reordered == TRUE) %>%
group_by(product_id) %>%
dplyr::summarise(freq = n()) %>%
top_n(10, wt = freq) %>%
left_join(products, by = 'product_id') %>%
ggplot(aes(x = reorder(product_name, - freq) , y = freq )) +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_col( color = "red") +
labs(title = "Top reordered items for train dataset") +
xlab("product name") +
ylab("Reordered frequency")
reordered_prior <- order_prior %>%
filter(reordered == TRUE) %>%
group_by(product_id) %>%
dplyr::summarise(freq = n()) %>%
top_n(10, wt = freq) %>%
left_join(products, by = 'product_id') %>%
ggplot(aes(x = reorder(product_name, - freq) , y = freq )) +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_col( color = "blue") +
labs(title = "Top reordered items for prior dataset") +
xlab("product name") +
ylab("Reordered frequency")
# plot P1, P2, P3, p4 in the same plot
layout <- matrix(c(1,2), 1, 2, byrow = TRUE)
multiplot(reordered_train, reordered_prior, layout=layout)
We find: The main difference is in Organic Whole Milk which is not in the top 10 products in train orders.
2.7 Which most items added the first to the card
first_item_to_cart_prior <-
order_prior %>%
filter(add_to_cart_order == 1) %>%
#filter(product_id == "345") %>%
group_by(product_id, reordered) %>%
dplyr::summarise(n_first = n()) %>%
arrange(desc(n_first)) %>%
head(10) %>%
# dplyr::top_n(10, wt = n_first) %>% doesn't work
left_join(products, by = 'product_id') %>%
ggplot(aes(x = product_name, y = n_first)) +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_col(aes(fill = reordered)) +
labs(title = "Top first added items to the cart (Prior)") +
xlab("product name") +
ylab("frequency of added the first to the cart")
first_item_to_cart_train <-
order_train %>%
filter(add_to_cart_order == 1) %>%
group_by(product_id, reordered) %>%
#summarize(proportion_reordered = mean(reordered), n=n())
dplyr::summarise(n_first = n()) %>%
arrange(desc(n_first)) %>%
head(10) %>%
# dplyr::top_n(10, wt = n_first) %>% doesn't work
left_join(products, by = 'product_id') %>%
ggplot(aes(x = product_name, y = n_first)) +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_col(aes(fill = reordered)) +
labs(title = "Top first added items to the cart (Train)") +
xlab("product name") +
ylab("frequency of added the first to the cart")
# plot P1, P2, P3, p4 in the same plot
layout <- matrix(c(1,2), 1, 2, byrow = TRUE)
multiplot(first_item_to_cart_prior, first_item_to_cart_train, layout=layout)
we find: In general only the reordered items are added at the bigening of shopping. Only few cases with banana are not ordered for the first time and added the first to the cart.
2.8 reordering rate versus added rank to the cart
order_train %>%
group_by(product_id, add_to_cart_order) %>%
summarize(n_items_basket = n(), reordered_rate_basket = mean(reordered)) %>%
group_by(add_to_cart_order) %>%
summarise(reordered_rate_all = mean(reordered_rate_basket))%>%
ggplot(aes(x= add_to_cart_order, y = reordered_rate_all)) +
geom_line()
2.9 Which most pourcentage of items added the first to the card
first_pct_item_to_cart_train <- order_train %>%
group_by(product_id, add_to_cart_order) %>%
summarize(count = n()) %>%
mutate(pct=count/sum(count)) %>%
filter(add_to_cart_order == 1, count>10) %>%
arrange(desc(pct)) %>%
left_join(products,by='product_id') %>%
ungroup() %>%
select(product_name, pct, count) %>%
top_n(10, wt=pct) %>%
ggplot(aes(x = reorder(product_name,-pct), y = pct)) +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_col() +
labs(title = "Top first % added items to the cart (Train)") +
xlab("Product name") +
ylab("Pourcentage (%) of item added the first to the cart")
first_pct_item_to_cart_prior <- order_prior %>%
group_by(product_id, add_to_cart_order) %>%
summarize(count = n()) %>%
mutate(pct=count/sum(count)) %>%
filter(add_to_cart_order == 1, count>10) %>%
arrange(desc(pct)) %>%
left_join(products,by='product_id') %>%
ungroup() %>%
select(product_name, pct, count) %>%
top_n(10, wt=pct) %>%
ggplot(aes(x = reorder(product_name,-pct), y = pct)) +
geom_label(stat = "count", aes(label = ..count.., y = ..count..), size=3)+
theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_col() +
labs(title = "Top first % added items to the cart (Prior)") +
xlab("Product name") +
ylab("Pourcentage (%) of item added the first to the cart")
# plot P1, P2, P3, p4 in the same plot
layout <- matrix(c(1,2), 1, 2, byrow = TRUE)
multiplot(first_pct_item_to_cart_prior, first_pct_item_to_cart_train, layout=layout)
2.10 Top90 sellers of Banana and Strawberries
banana_id <- products %>%
filter(product_name == "Banana") %>%
#select(product_id) %>%
.$product_id
strawberries_id <- products %>%
filter(product_name == "Strawberries") %>%
#select(product_id) %>%
.$product_id
spring_water_id <- products %>%
filter(product_name == "Spring Water") %>%
#select(product_id) %>%
.$product_id
asparagus_id <- products %>%
filter(product_name == "Asparagus") %>%
#select(product_id) %>%
.$product_id
# filter the orders with banana
order_train %>%
filter(product_id %in% c(banana_id, strawberries_id)) %>%
left_join(orders, by = "order_id") %>%
group_by( order_id, user_id) %>%
dplyr::summarise(n_orders = last(order_number)) %>%
filter(n_orders == 90) FALSE # A tibble: 6 x 3
FALSE # Groups: order_id [6]
FALSE order_id user_id n_orders
FALSE <int> <fct> <int>
FALSE 1 271953 145628 90
FALSE 2 1578927 24195 90
FALSE 3 1589791 188446 90
FALSE 4 1854209 166449 90
FALSE 5 2803296 179192 90
FALSE 6 2975947 195993 902.11 Explore Days interval of reordering items
interval_item_reordered_train <- order_train %>%
left_join(orders, by = "order_id") %>%
group_by(days_since_prior_order) %>%
summarize(mean_reorder = mean(reordered), n = n()) %>%
ggplot(aes(days_since_prior_order, y = mean_reorder)) +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_col( color = "red") +
labs(title = "Mean Interval (days) of reordered items (Train)") +
xlab("Days") +
ylab("Mean reordered (%)")
interval_item_reordered_prior <- order_prior %>%
left_join(orders, by = "order_id") %>%
group_by(days_since_prior_order) %>%
summarize(mean_reorder = mean(reordered), n = n()) %>%
ggplot(aes(days_since_prior_order, y = mean_reorder)) +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_col( color = "blue") +
labs(title = "Mean Interval (days) of reordered items (Prior)") +
xlab("Days") +
ylab("Mean reordered (%)")
# plot P1, P2, P3, p4 in the same plot
layout <- matrix(c(1,2), 1, 2, byrow = TRUE)
multiplot(interval_item_reordered_train, interval_item_reordered_prior, layout=layout)
With 0 days items are reordered at 0.85%. After 30 days the same items are ordered at 0.45%
2.12 Explore number of orders and reordering items
grp_pdt_train <- order_train %>%
#left_join(orders, by = "order_id") %>%
group_by(product_id) %>%
summarize(mean_reorder = mean(reordered), n = n()) %>%
ggplot(aes(n, y = mean_reorder)) +
#theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_point(size = 0.1, alpha = 0.3) +
geom_smooth(color="red")+
labs(title = "Number of reordered items per product_id (Train)") +
xlab("Number of orders") +
ylab("Mean reordered (%) per product_id") +
coord_cartesian(xlim=c(0,5000))
grp_order_train <- order_train %>%
#left_join(orders, by = "order_id") %>%
group_by(order_id) %>%
summarize(mean_reorder = mean(reordered), n = n()) %>%
ggplot(aes(n, y = mean_reorder)) +
#theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_point(color= "red", size = 0.1, alpha = 0.3) +
labs(title = "Number of reordered items per order_id (Train)") +
xlab("Number of orders") +
ylab("Mean reordered (%) per order_id")
grp_pdt_prior <- order_prior %>%
#left_join(orders, by = "order_id") %>%
group_by(product_id) %>%
summarize(mean_reorder = mean(reordered), n = n()) %>%
ggplot(aes(n, y = mean_reorder)) +
#theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_point( size = 0.1, alpha = 0.3) +
geom_smooth(color="blue")+
labs(title = "Number of reordered items per product_id (Prior)") +
xlab("Number of orders") +
ylab("Mean reordered (%) per product_id") +
coord_cartesian(xlim=c(0,10000))
grp_order_prior <- order_prior %>%
#left_join(orders, by = "order_id") %>%
group_by(order_id) %>%
summarize(mean_reorder = mean(reordered), n = n()) %>%
ggplot(aes(n, y = mean_reorder)) +
#theme(axis.text.x = element_text(angle=45, hjust=1)) +
geom_point(color="blue", size = 0.1, alpha = 0.3) +
labs(title = "Number of reordered items per order_id (Prior)") +
xlab("Number of orders") +
ylab("Mean reordered (%) per order_id")
# plot P1, P2, P3, p4 in the same plot
layout <- matrix(c(1,2, 3, 4), 2, 2, byrow = TRUE)
multiplot(grp_pdt_train, grp_order_train,grp_pdt_prior, grp_order_prior , layout=layout)FALSE `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
FALSE `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
2.13 Visualize Departments and Aisles
library(treemap)
items_per_aisle <- products %>%
group_by(department_id, aisle_id) %>%
summarize(n_items = n()) %>%
left_join(departments,by="department_id") %>%
left_join(aisles,by="aisle_id")
tree_aisle <- order_train %>%
group_by(product_id) %>%
dplyr::summarise(count_ordered_item = n()) %>%
left_join(products, by = "product_id") %>%
ungroup() %>%
group_by(department_id, aisle_id) %>%
summarize(sumcount = sum(count_ordered_item)) %>%
left_join(items_per_aisle, by = c("department_id", "aisle_id")) %>%
mutate(onesize = 1)
treemap(tree_aisle,index = c("department","aisle"),
vSize = "onesize",
vColor = "department",
palette = "Set3",
title = "super market Map",
sortID = "-sumcount",
border.col = "#FFFFFF",
type = "categorical",
fontsize.legend = 0,
bg.labels = "#FFFFFF")
treemap(tree_aisle,index = c("department","aisle"),
vSize = "sumcount",
vColor = "department",
palette = "Set3",
title = "super market Map",
# sortID = "-sumcount",
border.col = "#FFFFFF",
type = "categorical",
fontsize.legend = 0,
bg.labels = "#FFFFFF")
2.14 Look for proportion of costumers that reorders the same products
order_number_upper2 <- order_prior %>%
group_by(order_id) %>%
dplyr::summarise(mean_redordered_item_per_basket = mean(reordered), n_items_per_basket = n()) %>%
left_join(orders, by = "order_id") %>%
filter(order_number > 2)
order_number_upper2 %>%
#filter(eval_set =="prior") %>%
group_by(user_id) %>%
dplyr::summarise(sum_orders = sum(mean_redordered_item_per_basket == 1, na.rm = TRUE), ratio_reordered_items = sum_orders/n()) %>%
filter(ratio_reordered_items == 1) %>%
arrange(desc(sum_orders)) %>%
head(10)FALSE # A tibble: 10 x 3
FALSE user_id sum_orders ratio_reordered_items
FALSE <fct> <int> <dbl>
FALSE 1 99753 97 1
FALSE 2 55331 49 1
FALSE 3 106510 49 1
FALSE 4 111365 47 1
FALSE 5 74656 46 1
FALSE 6 170174 45 1
FALSE 7 12025 43 1
FALSE 8 164779 37 1
FALSE 9 37075 34 1
FALSE 10 110225 33 1Here i look for customers who just reorder the same products again all the time. To search those I look at all orders (excluding the first order), where the percentage of reordered items is exactly 1 (This can easily be adapted to look at more lenient thresholds). We can see there are in fact 3,487 customers, just always reordering products. user_id 99753 reorded the same items (same basket countain) for 97 visits/orders.
2.15 the basket for the most fidel user_id
order_number_upper2 %>%
filter(user_id == 99753) %>%
left_join(order_prior, by = "order_id") %>%
left_join(products, by = "product_id") %>%
select(product_name, user_id, order_id, w_day, days_since_prior_order) %>%
arrange(order_id) %>%
head(10)## # A tibble: 10 x 5
## product_name user_id order_id w_day days_since_prior_order
## <fct> <fct> <int> <ord> <dbl>
## 1 Organic Whole Milk 99753 46614 Tue 2
## 2 Organic Reduced Fat Milk 99753 46614 Tue 2
## 3 Organic Whole Milk 99753 67223 Wed 2
## 4 Organic Reduced Fat Milk 99753 67223 Wed 2
## 5 Organic Whole Milk 99753 214506 Sun 5
## 6 Organic Reduced Fat Milk 99753 214506 Sun 5
## 7 Organic Whole Milk 99753 240832 Tue 2
## 8 Organic Reduced Fat Milk 99753 240832 Tue 2
## 9 Organic Whole Milk 99753 260804 Sun 4
## 10 Organic Reduced Fat Milk 99753 260804 Sun 4The user_id buy always the same two items Organic milk, maybe for a baby.
orders %>%
left_join(sample_sub, "order_id") %>%
head(20)## order_id user_id eval_set order_number order_dow order_hour_of_day
## 1 2539329 1 prior 1 2 8
## 2 2398795 1 prior 2 3 7
## 3 473747 1 prior 3 3 12
## 4 2254736 1 prior 4 4 7
## 5 431534 1 prior 5 4 15
## 6 3367565 1 prior 6 2 7
## 7 550135 1 prior 7 1 9
## 8 3108588 1 prior 8 1 14
## 9 2295261 1 prior 9 1 16
## 10 2550362 1 prior 10 4 8
## 11 1187899 1 train 11 4 8
## 12 2168274 2 prior 1 2 11
## 13 1501582 2 prior 2 5 10
## 14 1901567 2 prior 3 1 10
## 15 738281 2 prior 4 2 10
## 16 1673511 2 prior 5 3 11
## 17 1199898 2 prior 6 2 9
## 18 3194192 2 prior 7 2 12
## 19 788338 2 prior 8 1 15
## 20 1718559 2 prior 9 2 9
## days_since_prior_order w_day products
## 1 NA Mon <NA>
## 2 15 Tue <NA>
## 3 21 Tue <NA>
## 4 29 Wed <NA>
## 5 28 Wed <NA>
## 6 19 Mon <NA>
## 7 20 Sun <NA>
## 8 14 Sun <NA>
## 9 0 Sun <NA>
## 10 30 Wed <NA>
## 11 14 Wed <NA>
## 12 NA Mon <NA>
## 13 10 Thu <NA>
## 14 3 Sun <NA>
## 15 8 Mon <NA>
## 16 8 Tue <NA>
## 17 13 Mon <NA>
## 18 14 Mon <NA>
## 19 27 Sun <NA>
## 20 8 Mon <NA>orders %>%
inner_join(sample_sub, "order_id") %>%
head(10)## order_id user_id eval_set order_number order_dow order_hour_of_day
## 1 2774568 3 test 13 5 15
## 2 329954 4 test 6 3 12
## 3 1528013 6 test 4 3 16
## 4 1376945 11 test 8 6 11
## 5 1356845 12 test 6 1 20
## 6 2161313 15 test 23 1 9
## 7 1416320 16 test 7 0 13
## 8 1735923 19 test 10 6 17
## 9 1980631 20 test 5 1 11
## 10 139655 22 test 16 5 6
## days_since_prior_order w_day products
## 1 11 Thu 39276 29259
## 2 30 Tue 39276 29259
## 3 22 Tue 39276 29259
## 4 8 Fri 39276 29259
## 5 30 Sun 39276 29259
## 6 7 Sun 39276 29259
## 7 7 <NA> 39276 29259
## 8 8 Fri 39276 29259
## 9 30 Sun 39276 29259
## 10 1 Thu 39276 29259